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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationWed, 05 Nov 2008 08:12:03 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/05/t1225897996jiz58frv0ok5yje.htm/, Retrieved Sun, 19 May 2024 08:47:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=21803, Retrieved Sun, 19 May 2024 08:47:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsjenske_cole@hotmail.com
Estimated Impact173

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Mean Plot] [workshop 3] [2007-10-26 12:14:28] [e9ffc5de6f8a7be62f22b142b5b6b1a8]
F RMPD    [Kendall tau Correlation Matrix] [opdracht 2 s1] [2008-11-05 15:12:03] [120dfa2440e51a0cfc0f5296bc5d7460] [Current]
-    D      [Kendall tau Correlation Matrix] [paper kendall tau...] [2008-12-13 11:56:47] [975daa21de49eaf4d491226310243f5a]
Feedback Forum
2008-11-09 15:57:44 [Vincent Vanden Poel] [reply] 
De grafiek is correct gereproduceerd. Ook de interpretatie is min of meer correct. Je zou er wel op kunnen wijzen dat probabiliteitsratio's betrouwbaar zijn als ze een waarde hebben die kleiner is dan 0,05 (P-value). Hier zijn dus twee mogelijke predictors voor de RNR nl: RNVM en RCF. Omdat de waarde van de RCF nog kleiner is dan die van de RNVM kunnen we besluiten dat deze eerste ratio de beste predictor is voor de RNR.
2008-11-10 10:10:23 [c97d2ae59c98cf77a04815c1edffab5a] [reply] 
Volgens mij is er wel wat aanvullend uitleg bij conclusie nodig.
Methode: kendall tau correlation matrix
- geeft alle correlaties tussen variabelen in 1keer
- betere maatstaf: veel robuuster en ondervindt minder invloed van outliers.
- Boven variabelen: verband/correlatie tussen 2 variabelen.
- Onder variabelen: waarschijnlijkheid dat verband aan toeval kan toegewezen worden.

in woorden: Kendall correlation: de coëfficiënt ondereen de variabelen is geen coëfficiënt voor de correlatie, MAAR een mate van probebaliteit => waarin correlatie aan toeval kan toegewezen worden. Tabel: tau=correlatiecoëfficiënt, P-value=probebaliteit.
Conclusie: We kunnen uit bovenstaande figuur besluiten dat de RCF(cashflow/EV) de beste voorspeller is voor RNR, omdat de correlatie hier het hoogst is en dit slechts voor 1% aan toeval kan toegewezen worden. Nochtans was dit niet verwacht.
2008-11-10 12:31:51 [Jenske Cole] [reply] 
Hoe groter het getal, hoe slechter de correlatie. Zoals bovenstaande persoon al zei zijn de voordelen dat het alle correlaties in 1 keer weergeeft, het een betere maatstaf is daar er minder invloed van outliners is. Voor RNR is RCF de beste voorspeller.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4.2	4.8	20.8	0.9	39.6
2.6	-4.2	17.1	0.85	36.1
3	1.6	22.3	0.83	34.4
3.8	5.2	25.1	0.84	33.4
4	9.2	27.7	0.85	34.8
3.5	4.6	24.9	0.83	33.7
4.1	10.6	29.5	0.83	36.3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21803&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21803&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21803&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Kendall tau rank correlations for all pairs of data series
pairtaup-value
tau( RNVM , RNR )0.7142857142857140.0301587301587301
tau( RNVM , RCF )0.5238095238095240.136111111111111
tau( RNVM , RLEZ )0.2646280620124820.427262856745706
tau( RNVM , REV )0.3333333333333330.381349206349206
tau( RNR , RCF )0.809523809523810.0107142857142857
tau( RNR , RLEZ )-0.05292561240249630.873844698517373
tau( RNR , REV )0.04761904761904761
tau( RCF , RLEZ )-0.2646280620124820.427262856745706
tau( RCF , REV )-0.1428571428571430.772619047619048
tau( RLEZ , REV )0.3704792868174740.266379923342483

\begin{tabular}{lllllllll}
\hline
Kendall tau rank correlations for all pairs of data series \tabularnewline
pair & tau & p-value \tabularnewline
tau( RNVM , RNR ) & 0.714285714285714 & 0.0301587301587301 \tabularnewline
tau( RNVM , RCF ) & 0.523809523809524 & 0.136111111111111 \tabularnewline
tau( RNVM , RLEZ ) & 0.264628062012482 & 0.427262856745706 \tabularnewline
tau( RNVM , REV
 ) & 0.333333333333333 & 0.381349206349206 \tabularnewline
tau( RNR , RCF ) & 0.80952380952381 & 0.0107142857142857 \tabularnewline
tau( RNR , RLEZ ) & -0.0529256124024963 & 0.873844698517373 \tabularnewline
tau( RNR , REV
 ) & 0.0476190476190476 & 1 \tabularnewline
tau( RCF , RLEZ ) & -0.264628062012482 & 0.427262856745706 \tabularnewline
tau( RCF , REV
 ) & -0.142857142857143 & 0.772619047619048 \tabularnewline
tau( RLEZ , REV
 ) & 0.370479286817474 & 0.266379923342483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21803&T=1

[TABLE]
[ROW][C]Kendall tau rank correlations for all pairs of data series[/C][/ROW]
[ROW][C]pair[/C][C]tau[/C][C]p-value[/C][/ROW]
[ROW][C]tau( RNVM , RNR )[/C][C]0.714285714285714[/C][C]0.0301587301587301[/C][/ROW]
[ROW][C]tau( RNVM , RCF )[/C][C]0.523809523809524[/C][C]0.136111111111111[/C][/ROW]
[ROW][C]tau( RNVM , RLEZ )[/C][C]0.264628062012482[/C][C]0.427262856745706[/C][/ROW]
[ROW][C]tau( RNVM , REV
 )[/C][C]0.333333333333333[/C][C]0.381349206349206[/C][/ROW]
[ROW][C]tau( RNR , RCF )[/C][C]0.80952380952381[/C][C]0.0107142857142857[/C][/ROW]
[ROW][C]tau( RNR , RLEZ )[/C][C]-0.0529256124024963[/C][C]0.873844698517373[/C][/ROW]
[ROW][C]tau( RNR , REV
 )[/C][C]0.0476190476190476[/C][C]1[/C][/ROW]
[ROW][C]tau( RCF , RLEZ )[/C][C]-0.264628062012482[/C][C]0.427262856745706[/C][/ROW]
[ROW][C]tau( RCF , REV
 )[/C][C]-0.142857142857143[/C][C]0.772619047619048[/C][/ROW]
[ROW][C]tau( RLEZ , REV
 )[/C][C]0.370479286817474[/C][C]0.266379923342483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21803&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21803&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Kendall tau rank correlations for all pairs of data series
pairtaup-value
tau( RNVM , RNR )0.7142857142857140.0301587301587301
tau( RNVM , RCF )0.5238095238095240.136111111111111
tau( RNVM , RLEZ )0.2646280620124820.427262856745706
tau( RNVM , REV )0.3333333333333330.381349206349206
tau( RNR , RCF )0.809523809523810.0107142857142857
tau( RNR , RLEZ )-0.05292561240249630.873844698517373
tau( RNR , REV )0.04761904761904761
tau( RCF , RLEZ )-0.2646280620124820.427262856745706
tau( RCF , REV )-0.1428571428571430.772619047619048
tau( RLEZ , REV )0.3704792868174740.266379923342483


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
R code (references can be found in the software module):
panel.tau <- function(x, y, digits=2, prefix='', cex.cor)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
rr <- cor.test(x, y, method='kendall')
r <- round(rr$p.value,2)
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep='')
if(missing(cex.cor)) cex <- 0.5/strwidth(txt)
text(0.5, 0.5, txt, cex = cex)
}
panel.hist <- function(x, ...)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Kendall tau rank correlations for all pairs of data series',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'pair',1,TRUE)
a<-table.element(a,'tau',1,TRUE)
a<-table.element(a,'p-value',1,TRUE)
a<-table.row.end(a)
n <- length(y[,1])
n
cor.test(y[1,],y[2,],method='kendall')
for (i in 1:(n-1))
{
for (j in (i+1):n)
{
a<-table.row.start(a)
dum <- paste('tau(',dimnames(t(x))[[2]][i])
dum <- paste(dum,',')
dum <- paste(dum,dimnames(t(x))[[2]][j])
dum <- paste(dum,')')
a<-table.element(a,dum,header=TRUE)
r <- cor.test(y[i,],y[j,],method='kendall')
a<-table.element(a,r$estimate)
a<-table.element(a,r$p.value)
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable.tab')